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YOLOv3 training, preprocessing, validation, and inference for object detection in xView satellite imagery and the xView detection challenge.
| Date | Stars |
|---|---|
| 2026-07-24 | 338 |
| 2026-07-25 | 338 |
| 2026-07-28 | 338 |
| 2026-07-30 | 338 |
| 2026-08-06 | 338 |
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<a href="https://www.ultralytics.com/"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a> # 🚀 Introduction Welcome to the Ultralytics `xview-yolov3` repository! This project provides the necessary code and instructions to train the powerful [Ultralytics YOLOv3](https://docs.ultralytics.com/models/yolov3/) object detection model on the challenging [xView dataset](https://challenge.xviewdataset.org/). The primary goal is to support participants in the [xView Challenge](https://challenge.xviewdataset.org/), which focuses on advancing the state-of-the-art in detecting objects within [satellite imagery](https://www.ultralytics.com/blog/using-computer-vision-to-analyse-satellite-imagery), a critical application of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) in remote sensing. [](https://github.com/ultralytics/xview-yolov3/actions/workflows/format.yml) [](https://discord.com/invite/ultralytics) [](https://community.ultralytics.com/) [](https://reddit.com/r/ultralytics) <img src="https://github-production-user-asset-6210df.s3.amazonaws.com/26833433/238799379-bb3b02f0-dee4-4e67-80ae-4b2378b813ad.jpg?raw=true" width="100%" alt="xView dataset example detections"> # 📦 Requirements To successfully run this project, ensure your environment meets the following prerequisites: - **Python:** Version 3.6 or later. You can download Python from the [official Python website](https://www.python.org/downloads/). - **Dependencies:** Install the required packages using pip. It's recommended to use a virtual environment. ```bash pip3 install -U -r requirements.txt ``` Key dependencies include: - `numpy`: Essential for numerical operations in Python. - `scipy`: Provides algorithms for scientific and technical computing. - `torch`: The core [PyTorch](https://pytorch.org/) library for deep learning. - `opencv-python`: The [OpenCV](https://opencv.org/) library for computer vision tasks. - `h5py`: Enables interaction with data stored in HDF5 format. - `tqdm`: A utility for displaying progress bars in loops and command-line interfaces. # 📥 Download Data Begin by downloading the necessary xView dataset files. You can obtain the data directly from the [xView Challenge data download page](https://challenge.xviewdataset.org/data-download). Ensure you have sufficient storage space, as satellite imagery datasets can be quite large. # 🏋️♂️ Training Training the YOLOv3 model on the xView dataset involves preprocessing the data and then running the training script. ## Preprocessing Steps Before initiating the training process, we perform several preprocessing steps on the target labels to enhance model performance: 1. **Outlier Removal:** Outliers in the dataset are identified and removed using sigma-rejection to clean the data. 2. **Anchor Generation:** A new set of 30 [k-means anchors](https://www.ultralytics.com/glossary/anchor-based-detectors) are generated specifically tailored for the `c60_a30symmetric.cfg` configuration file. This process utilizes the MATLAB script `utils/analysis.m`. The generated anchors help the model better predict bounding boxes of various sizes and aspect ratios present in the xView dataset. <img src="https://github.com/ultralytics/xview-yolov3/blob/main/cfg/c60_a30.png?raw=true" width="500" alt="k-means anchors plot"> ## Starting the Training Once the xView data is down
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matched fp:28935dfd23341c55, topic:computer-vision, topic:object-detection, readme:computer vision